Contextual Sequence Prediction with Application to Control Library Optimization
Debadeepta Deya b, Tian Yu Liua b, Martial Heberta b, James Andrew Bagnella c
- aThe Robotics Institute Carnegie Mellon University , Pittsburgh , PA
- bCarnegie Mellon University, Pittsburgh, United States
- cCarnegie Mellon University
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Abstract
Sequence optimization, where the items in a list are ordered to maximize some reward has many applications such as web advertisement placement, search, and control libraries in robotics.Previous work in sequence optimization produces a static ordering that does not take any features of the item or context of the problem into account.In this work, we propose a general approach to order the items within the sequence based on the context (e.g., perceptual information, environment description, and goals).We take a simple, efficient, reduction-based approach where the choice and order of the items is established by repeatedly learning simple classifiers or regressors for each "slot" in the sequence.Our approach leverages recent work on submodular function maximization to provide a formal regret reduction from submodular sequence optimization to simple costsensitive prediction.We apply our contextual sequence prediction algorithm to optimize control libraries and demonstrate results on two robotics problems: manipulator trajectory prediction and mobile robot path planning.
TL;DRThis work proposes a general approach to order the items within the sequence based on the context, and takes a simple, efficient, reduction-based approach where the choice and order of the items is established by repeatedly learning simple classifiers or regressors for each “slot” in the sequence. (Semantic Scholar)